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24 use cases using this technology · by Amazon Web Services

24published use cases
12industries
8countries on record
Generative AI
top AI capability

Evidence mix: High 24 · Medium 0 · Low 0 — bands are computed from each record's evidence signals.

Industry
Country

24 use cases

ManufacturingComputer VisionPredictive Analytics

ŠKODA's MAGIC EYE computer vision system predicts and prevents production line breakdowns

ŠKODA AUTO built MAGIC EYE, a computer-vision predictive maintenance system, using AWS Internet of Things services and in-house technology to move from a reactive to a proactive approach to assembly line breakdowns. Six cameras mounted by a conveyor frame collect nearly 450,000 photos as a car moves through the production line, and 10 artificial neural networks analyze them to detect irregularities such as dirt, loose or cracked bolts, or aluminum track damage, alerting a maintenance operator. The neural networks can recognize 14 defect types and 178 classes, and AWS lists reduced assembly line downtime and increased staff productivity among the benefits.

ŠKODA AUTO· Czech RepublicAmazon S3 · Amazon EC2 · Amazon RDS +1
Media & EntertainmentGenerative AIMachine LearningLarge Language Models

Launchmetrics uses generative AI to cut brand-analytics prototype development from 5 months to weeks

Launchmetrics, an AI-driven marketing analytics company for the fashion, lifestyle, and beauty sectors, uses Amazon Bedrock generative AI in a two-step process: first using machine learning to identify keywords describing brands across tens of millions of chunks of online text, then using large language models to score identified trends against brand dimensions and translate the analysis into narratives marketing professionals can understand without data science expertise. Using generative AI on AWS, Launchmetrics sped up prototype development from as long as 5 months to just a few weeks, and gained the ability to generate synthetic data sets for brands with little online presence, allowing it to move from managing hundreds of brands with AI to thousands.

LaunchmetricsAmazon Bedrock · Amazon S3
AutomotiveComputer VisionSpeech & Audio AIPredictive Analytics

BMW Group uses AI-powered vision and sound analytics for predictive maintenance and quality management

The BMW Group built its Vision and Sound Analytics Service (VSAS) on AWS, processing 1.3 million image and audio files daily across 1.3 PB of data at more than 16 production sites worldwide. The company uses the platform's stored data to build AI models for predictive maintenance and quality management, including a computer vision model that predicts potential rail failures to reduce downtime, and an Acoustic Analytics application that automatically captures and analyzes vehicle sounds using trained models to detect anomalies and improve engine performance and in-cabin comfort, reducing the need for human intervention in testing vehicles with no driver present. Using AWS Graviton processors for AI model training, BMW cut compute costs for its computer vision solution by 63% and reduced model training time for various use cases from days to hours.

BMW GroupAmazon EC2 · Amazon S3 · Amazon OpenSearch Service +4
Life SciencesComputer VisionMachine Learning

Novo Nordisk uses machine learning and computer vision to automate pharmaceutical manufacturing quality checks

Novo Nordisk, a Danish pharmaceutical company that supplies nearly 50 percent of the world's insulin, built an automated ML pipeline on AWS using Amazon SageMaker Pipelines to train, deploy, and monitor computer vision models on edge devices. The system automates previously manual tasks including counting drug cartridges in boxes and detecting bacterial anomalies on agar plates, using a robotic arm, camera rig, and edge inference, with results monitored via an Amazon QuickSight dashboard. The solution deploys ML models at scale to different edge devices, automates quality-assurance tasks, improves time to market, and was successfully repurposed from the cartridge-counting use case to the agar-plate anomaly detection use case with minimal changes.

Novo Nordisk· DenmarkAmazon SageMaker · Amazon S3 · AWS IoT Greengrass +1
Financial ServicesGenerative AIComputer VisionDocument Intelligence

Empowering Employees to Work Strategically Using Amazon Bedrock with BDM

Big Data Mining (BDM), a Brazilian company, built LOUIS, a bespoke generative AI model on Amazon Bedrock combining computer vision and natural language processing to process complex, unstructured corporate documents (contracts, powers of attorney) across more than 100 models and 15 industries. A large Brazilian financial institution deployed LOUIS to replace a team of 150 professionals manually evaluating over 40,000 unstructured processes per month for opening legal-entity accounts and contracting credit, standardizing interpretation criteria and reducing operational effort by more than 40%, an estimated $4.2 million in cost savings over five years. In insurance, LOUIS reduced life insurance claim processing from up to six months to just a few minutes by capturing required data in about 60 seconds. BDM reports documents are processed 85% faster than manual interpretation with 98% accuracy, and saved roughly 50% on development costs by building on Amazon Bedrock rather than writing the application from scratch.

Big Data Mining (BDM)· BrazilAmazon Bedrock · Amazon S3 · Amazon EC2 +1
Financial ServicesGenerative AIPredictive Analytics

Discover Financial Services Builds a Generative AI Solution on AWS for Faster Decision-Making and Time to Market

Discover Financial Services, a digital banking and payment services company, built an analytics workbench and unified data science workbench on Amazon EC2 (P3 and P4 GPU instances) so its data scientists could run machine learning and generative AI workloads, train large language models with sample sizes requiring up to 6 TB of memory, and deliver high-performance computing in the cloud. Model artifacts are stored in Amazon S3 and shared across engineering teams via Amazon EFS. Discover used the platform for use cases including sentiment analysis of customer service calls and a 'do not contact' model that classifies customers in near real time for customer care agents. Using feature embedding, the team reduced time to market from hours to minutes; parallel model training cut processing of 30 million records from days to hours, and sentiment analysis on a 57,000-record dataset dropped from hours to minutes. Discover reports 35% reduction in engineering and platform costs.

Discover Financial Services· United StatesAmazon EC2 · Amazon S3 · Amazon EFS
EducationMachine LearningFraud & Anomaly DetectionAI Model Development & MLOps

GoGuardian: Safer schools, empowered teachers, thriving students

GoGuardian, which powers safe, focused learning for half of U.S. K-12 students, migrated its ML infrastructure to Databricks to manage billions of daily inferences for web filtering, classroom management and harm prevention while maintaining a PII-free, COPPA/FERPA-compliant data environment. Using Delta Lake, Lakeflow, Unity Catalog, MLflow and Databricks Model Serving, GoGuardian achieved up to 50% reduction in machine learning operational costs, 90% operational cost savings with its Delphi website classification model, and a 62% reduction in inappropriate device use among students. AI-driven prioritization also cut the volume of records requiring human review for high-risk content by over 95%, from 1 million to 35,000-45,000.

GoGuardian· United StatesDelta Lake · Databricks Lakeflow · Unity Catalog +6
ManufacturingComputer VisionFraud & Anomaly Detection

Siemens Electronics Factory Erlangen Reduces Machine Learning Deployment Time by 80% with AWS and Siemens Industrial AI on Industrial Edge

Siemens Electronics Factory Erlangen, which manufactures PCBs and controllers such as SINAMICS converters and SINUMERIK CNC controllers, used computer vision models to spot anomalies in PCB assembly, but training and retraining ML models on premises was time-intensive and constrained by GPU bottlenecks. The factory adopted AWS services together with Siemens Industrial Edge and Siemens Industrial AI, sending shopfloor images via Edge applications to Amazon S3 before training via Amazon SageMaker or AWS Lambda; training results and edge prediction results are monitored through AI Model Monitor, with AI Model Manager providing central management of models on the shopfloor. This reduced time spent on model training and retraining by 80 percent (from about 30 minutes to roughly 5 minutes for retraining and deployment), cut costs by more than 90 percent compared to on-premises data storage systems, and reduced the false call rate by over 50 percent, while continuously preventing around 4 percent of PCB assembly errors compared to around 60 percent at peaks in the past.

Siemens Electronics Factory ErlangenAmazon S3 · Amazon SageMaker · AWS Lambda +5
Government & Public SectorGenerative AINatural Language Processing

Contra Costa County District Attorney's Office Makes Unbiased Charging Decisions with ScaleCapacity Generative AI Solution on AWS

The Contra Costa County District Attorney's Office worked with AWS Partner ScaleCapacity to build a Race-Blind Charging solution to comply with California's AB 2778 mandate. The solution uses Amazon Bedrock and Amazon Textract to automatically redact race, ethnicity and other identifying details from police reports and case documents before charging decisions are made, with Amazon S3, DynamoDB, Cognito and SES supporting document storage, metadata and user access. The office processes around 17,000 cases annually, achieved compliance in six months, and can test and deploy new redaction rule changes in under a week.

Contra Costa County District Attorney's Office· United StatesAmazon Bedrock · Amazon Textract · Amazon S3 +3
Life SciencesDocument IntelligenceRetrieval-Augmented GenerationAgentic AI

Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation

Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.

NovartisAmazon Web Services (AWS) · Databricks · Amazon S3 +8